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And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life.

To the best of our knowledge, no study has analyzed the association between cigarette smoking and prostate basal cell proliferation. Therefore, we sought to evaluate whether smoking status is associated with the presence of basal cell hyperplasia (BCH).

We performed a retrospective analysis of 8,196 men aged 50 to 75 years with prostate-specific antigen values between 2.5 µg/mL and 10 µg/mL and prior negative biopsy who were enrolled in the (REDUCE) trial. Cigarette smoking status was divided into current, former, or never categories at enrollment. The association between smoking and baseline BCH was evaluated, with logistic regression in univariable and multivariable analysis.

A total of 1,233 (15.1%) men were current smokers, 3,206 (39.1%) were former smokers, and 3,575 (45.8%) were never smokers. In univariable analysis, current smoking was associated with higher baseline BCH occurrence compared with never (odds ratio [OR], 1.87; 95% confidence interval [CI], 1.14-3.10) and former smokers (OR, 1.77; 95% CI, 1.06-2.95). Similar results were found after adjusting for patient characteristics (current vs never smokers OR, 1.92; 95% CI, 1.14-3.26; current vs former smokers OR, 1.71; 95% CI, 1.01-2.91).

Among men undergoing prostate biopsy, all of whom had a negative biopsy result, current smoking at enrollment was independently associated with BCH in standard peripheral zone prostate biopsies.

Among men undergoing prostate biopsy, all of whom had a negative biopsy result, current smoking at enrollment was independently associated with BCH in standard peripheral zone prostate biopsies.[This corrects the article DOI 10.1093/geroni/igaa057.3519.].[This corrects the article DOI 10.1148/ryai.2020190211.].[This corrects the article DOI 10.1107/S1600536807044108.].[This corrects the article DOI 10.1007/s43465-020-00248-7.].Evidence supports various roles for microbial metabolites in the control of multiple aspects of host energy flux including feeding behaviors, digestive efficiency, and energy expenditure, but few studies have quantified the energy utilization of the biomass of the gut microbiota itself. Because gut microbiota exist in an anoxic environment, energy flux is expected to be anaerobic; unfortunately, commonly utilized O2/CO2 respirometry-based approaches are unable to detect anaerobic energy flux. To quantify the contribution of the gut microbial biomass to whole-animal energy flux, we examined the effect of surgical reduction of gut biomass in C57BL/6J mice via cecectomy and assessed energy expenditure using methods sensitive to anaerobic flux, including bomb and direct calorimetry. First, we determined that cecectomy caused an acceleration of weight gain over several months due to a reduction in combined total host plus microbial energy expenditure, as reflected by an increase in energy efficiency (ie, weight gained per calorie absorbed). Second, we determined that under general anesthesia, cecectomy caused immediate changes in heat dissipation that were significantly modified by short-term pretreatment with dietary or pharmaceutical interventions known to modify the microbiome, and confirmed that these effects were undetectable by respirometry. We conclude that while the cecum only contributes approximately 1% of body mass in the mouse, this organ contributes roughly 8% of total resting energy expenditure, that this contribution is predominantly anaerobic, and that the composition and abundance of the cecal microbial contents can significantly alter its contribution to energy flux.[This corrects the article DOI 10.1093/geroni/igaa057.1328.].Increase in travel time, beyond a critical point, to emergency care may lead to a residential disparity in the outcome of patients with acute conditions. However, few studies have evaluated the evidence of travel time benchmarks in view of the association between travel time and outcome. Thus, this study aimed to establish the optimal hospital access time (OHAT) for emergency care in South Korea. We used nationwide healthcare claims data collected by the National Health Insurance System database of South Korea. Claims data of 445,548 patients who had visited emergency centers between January 1, 2006 and December 31, 2014 were analyzed. Travel time, by vehicle from the residence of the patient, to the emergency center was calculated. Thirteen emergency care-sensitive conditions (ECSCs) were selected by a multidisciplinary expert panel. The 30-day mortality after discharge was set as the outcome measure of emergency care. A change-point analysis was performed to identify the threshold where the mortality of ECSCs changed significantly. The differences in risk-adjusted mortality between patients living outside of OHAT and those living inside OHAT were evaluated. Five ECSCs showed a significant threshold where the mortality changed according to their OHAT. These were intracranial injury, acute myocardial infarction, other acute ischemic heart disease, fracture of the femur, and sepsis. Crenolanib The calculated OHAT were 71-80 min, 31-40 min, 70-80 min, 41-50 min, and 61-70 min, respectively. Those who lived outside the OHAT had higher risks of death, even after adjustment (adjusted OR 1.04-7.21; 95% CI 1.03-26.34). In conclusion, the OHAT for emergency care with no significant increase in mortality is in the 31-80 min range. Optimal travel time to hospital should be established by optimal time for outcomes, and not by geographic time, to resolve the disparities in geographical accessibility to emergency care.